Neural network computers, are biologically inspired, that is, they are composed of elements that perform in a manner analogous to the most elementary functions of the biological neuron. In one methodology, a neural network computer is composed of a number (n) of processing elements that may be switches or nonlinear amplifiers. These elements are then organized in a way that may be related to the anatomy of the brain. The configuration of connections, and thus communication routes, between these elements represents the manner in which the neural network computer will function, analogous to that of a program performed by digital computers. Despite this superficial resemblance, such artificial neural networks exhibit a surprising number of the brain's characteristics. For example, they learn from experience, generalize from previous examples to new ones, and abstract essential characteristics from inputs containing irrelevant data. Unlike a von Neumann computer, such a neural network computer does not execute a list of commands (a program). Rather, it performs pattern recognition and associative recall via self-organization of connections between elements.
Artificial neural networks can modify their behavior in response to their environment. Shown a set of inputs (perhaps with desired outputs), they self-adjust to produce consistent responses. A network is trained so that application of a set of inputs produces the desired (or at least consistent) set of outputs. Each such input (or output) set is referred to as a vector. Training can be accomplished by sequentially applying input vectors, while adjusting network weights according to a predetermined procedure, or by setting weights a priori. During training, the network weights gradually converge to values such that each input vector produces the desired output vector.
Because of their ability to simulate the apparently oscillatory nature of brain neurons, oscillatory neural network computers are among the more promising types of neural network computers. Simply stated, an oscillatory neural network computer includes oscillators. Oscillators are mechanical, chemical or electronic devices that are described by an oscillatory signal (periodic, quasi-periodic, almost periodic function, etc.) Usually the output is a scalar function of the form V(ωt+φ) where V is a fixed wave form (sinusoid, saw-tooth, or square wave), ω is the frequency of oscillation, and φ is the phase deviation (lag or lead).
Recurrent neural networks have feedback paths from their outputs back to their inputs. The response of such networks is dynamic in that after applying a new input, the output is calculated and fed back to modify the input. The output is then recalculated, and the process is repeated again and again. Ideally, successive iterations produce smaller and smaller output changes until eventually the outputs become steady oscillations or reach a steady state. Although these techniques have provided a means for recognizing signals, to date they have not been able to do so using associative memory.
Accordingly, a need exists for a neural network computer with fully recurrent capabilities and a method that incorporates the periodic nature of neurons in the pattern recognition process.